Stochastic Text Models for Music Categorization
| Autor: | David Rizo, Carlos Pérez-Sancho, José M. Iñesta |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Perplexity
business.industry Computer science 05 social sciences Pop music automation 02 engineering and technology computer.software_genre 050105 experimental psychology Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Categorization 0202 electrical engineering electronic engineering information engineering Chord (music) Music information retrieval 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Language model Artificial intelligence business Jazz computer Natural language processing |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783540896883 SSPR/SPR ResearcherID |
| DOI: | 10.1007/978-3-540-89689-0_10 |
| Popis: | Music genre meta-data is of paramount importance for the organization of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research. This work brings to symbolic music recognition some technologies, like the stochastic language models, already successfully applied to text categorization. In this work we model chord progressions and melodies as n -grams and strings and then apply perplexity and naive Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Also a combination of the different techniques as an ensemble of classifiers is proposed. Some genres and sub-genres among popular, jazz, and academic music have been considered. The results show that the ensemble is a good trade-off approach able to perform well without the risk of choosing the wrong classifier. |
| Databáze: | OpenAIRE |
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